A Computational Framework for Understanding HCV Infection and Associated Lipid Dynamics

Abstract

Hepatitis C virus (HCV) infection is strongly intertwined with host lipid metabolism and is frequently associated with hepatic steatosis, particularly in genotype-specific cases where lipid accumulation reflects direct viral effects rather than host metabolic comorbidities. Despite extensive clinical and experimental evidence, a quantitative framework capturing the bidirectional coupling between viral dynamics and lipid metabolism remains lacking. In this work, we develop a comprehensive mathematical model that integrates HCV infection dynamics with intracellular lipid regulation. The framework combines a reaction diffusion system for spatial viral spread with a coupled metabolic module describing lipid influx. Numerical simulations highlight the role of lipid feedback in shifting infection outcomes toward persistence and recurrent oscillations, demonstrating that lipid accumulation acts as a critical driver of viral amplification. The model also highlights the critical role of immune memory to study acute and chronic phase and successfully reproduces observed viral RNA trajectories. Overall, the framework provides mechanistic insights into HCV dynamics and offers a general modeling approach for studying virus metabolism interactions in liver disease.